deds.stat.linkC integrates different statistics of differential
expression (DE) to rank and select a set of DE genes.

Usage

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Arguments

X

A matrix, with m rows corresponding to variables
(hypotheses) and n columns corresponding to observations.
In the case of gene expression data, rows correspond to genes and
columns to mRNA samples. The data can be read using read.table.

L

A vector of integers corresponding to observation (column)
class labels. For k classes, the labels must be integers
between 0 and k-1.

B

The number of permutations. For a complete enumeration,
B should be 0 (zero) or any number not less than the total
number of permutations.

tests

A character vector specifying the statistics to be
used to test the null hypothesis of no association between the
variables and the class labels, test could be any of the
following:

"t":

one or two sample t-statistics;

"f":

F-statistics;

"fc":

fold changes among classes;

"sam":

SAM-statistics;

"modt":

moderated t-statistics;

"modt":

moderated F-statistics;

"B":

B-statistics.

tail

A character string specifying the type of rejection
region.
If side="abs", two-tailed tests, the null hypothesis is
rejected for large absolute values of the test statistic.
If side="higher", one-tailed tests, the null hypothesis
is rejected for large values of the test statistic.
If side="lower", one-tailed tests, the null hypothesis is
rejected for small values of the test statistic.

extras

Extra parameter needed for the test specified; see
deds.genExtra.

distance

A character string specifying the type of distance
measure used for the calculation of the distance to the extreme
point (E).
If distance="weuclid", weighted euclidean distance, the
weight for statistic t is 1/MAD(t);
If distance="euclid", euclidean distance.

adj

A character string specifying the type of multiple testing
adjustment.
If adj="fdr", False Discovery Rate is controled and q
values are returned.
If adj="adjp", ajusted p values that controls family wise
type I error rate are returned.

nsig

If adj = "fdr", nsig specifies the number of top
differentially expressed genes whose q values will be calculated; we recommend
setting nsig < m, as the computation of q values will be extensive. q values
for the rest of genes will be approximated to 1. If adj = "adjp", the
calculation of the adjusted p values will be for the whole dataset.

quick

A logical variable specifying if a quick but memory
requiring procedure will be selected. If quick=TRUE,
permutation will be carried out once and stored in memory; If
quick=FALSE a fixed seeded sampling procedure will be
employed, which requires more computation time as the permutation
will be carried out twice, but will not use extra memory for storage.

Details

deds.stat.linkC summarizes multiple statistical measures for the
evidence of DE. The DEDS methodology treats each gene as
a point corresponding to a gene's vector of DE measures. An "extreme
origin" is defined as the maxima of all statistics and the
distance from all points to the extreme is computed and ranking of
a gene for DE is determined by the closeness of the gene to the
extreme. To determine a cutoff for declaration of DE, null referent
distributions are generated by permuting the data matrix.

Statistical measures currently in the DEDS package include t statistics
(tests="t"), fold changes (tests="fc"), F
statistics (tests="f"), SAM (tests="sam"), moderated
t (tests="modt"), moderated F statistics
(tests="modf"), and B statistics (tests="B"). The
function deds.stat.linkC interfaces to C functions for the
tests and the computation of DEDS. For more flexibility, the user can
also use deds.stat which has the same functionality as
deds.stat.linkC but is written completely in R (therefore
slower) and the user can supply their own function for a statistic
not covered in the DEDS package.

DEDS can also summarize p values from different statistical models, see
deds.pval.